Researchers at NYU and U Michigan have published a paper explaining how they used a pair of machine-learning systems to develop a "universal fingerprint" that can fool the lowest-security fingerprint sensors 76% of the time (it is less effective against higher-security sensors).
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In 2009, the National Academies of Science published a massive report on forensics. For many Americans, forensics is possibly the most familiar of all the sciences. It's the one we welcome into our living rooms every night, along with TV crime dramas and murder mysteries. But the report's conclusions might surprise you.
For one thing, it's hard to even generalize about the state of forensic science in the United States, because everything from standard practices to accreditation varies widely by sub-discipline, law-enforcement agency, and whether the law enforcement is happening at a local, state, or federal level. Worse, it's not at all clear that some of those sub-disciplines have a sound, scientific basis. For instance, DNA analysis tends to be pretty well-supported by evidence, while fingerprint analysis remains an art, more dependent on the person looking at the fingerprint than on hard laws of anatomy. Of course, the report also found that there simply hasn't been enough research done to determine how scientific most disciplines of forensic science are to begin with. Meanwhile, the Supreme Court has allowed trial judges to certify forensic techniques as reliable even though we don't know whether they they are or not — and those decisions have been made in a haphazard, inconsistent way from one judge to the next.
Given how much our legal system relies on this stuff, we should all be feeling more than a little uncomfortable right about now. The state of forensic science, combined with its importance, virtually guarantees that there are innocent people behind bars (or worse) and criminals on the loose. Read the rest